LiteEFG: An Efficient Python Library for Solving Extensive-form Games
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866907998217043968 |
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| author | Liu, Mingyang Farina, Gabriele Ozdaglar, Asuman |
| author_facet | Liu, Mingyang Farina, Gabriele Ozdaglar, Asuman |
| contents | LiteEFG is an efficient library with easy-to-use Python bindings, which can solve multiplayer extensive-form games (EFGs). LiteEFG enables the user to express computation graphs in Python to define updates on the game tree structure. The graph is then executed by the C++ backend, leading to significant speedups compared to running the algorithm in Python. Moreover, in LiteEFG, the user needs to only specify the computation graph of the update rule in a decision node of the game, and LiteEFG will automatically distribute the update rule to each decision node and handle the structure of the imperfect-information game. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_20351 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LiteEFG: An Efficient Python Library for Solving Extensive-form Games Liu, Mingyang Farina, Gabriele Ozdaglar, Asuman Computer Science and Game Theory Artificial Intelligence Machine Learning LiteEFG is an efficient library with easy-to-use Python bindings, which can solve multiplayer extensive-form games (EFGs). LiteEFG enables the user to express computation graphs in Python to define updates on the game tree structure. The graph is then executed by the C++ backend, leading to significant speedups compared to running the algorithm in Python. Moreover, in LiteEFG, the user needs to only specify the computation graph of the update rule in a decision node of the game, and LiteEFG will automatically distribute the update rule to each decision node and handle the structure of the imperfect-information game. |
| title | LiteEFG: An Efficient Python Library for Solving Extensive-form Games |
| topic | Computer Science and Game Theory Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2407.20351 |